library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)
# read in data, clean column names
us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>%
clean_names()
renew_clean <- us_renew %>%
mutate(description = str_to_lower(description)) %>%
filter(str_detect(description, pattern = "consumption")) %>%
filter(!str_detect(description, pattern = "total"))
# lubridate autofills and nonsensical dates with NA, how great!
renew_date <- renew_clean %>%
mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>%
mutate(month_sep = yearmonth(yr_mo_day)) %>%
mutate(value = as.numeric(value)) %>%
drop_na(month_sep, value)
# make a version where month and year are in separate columns to use later
renew_parsed <- renew_date %>%
mutate(month = month(yr_mo_day, label = TRUE)) %>%
mutate(year = year(yr_mo_day))
renew_gg <- ggplot(data = renew_date, aes(x = month_sep,
y = value,
group = description))+
geom_line(aes(color = description))
renew_gg # saved graph with this name, can keep building by just referencing this name
Updating colors with paletteer palettes:
renew_gg +
scale_color_paletteer_d("nationalparkcolors::CraterLake")
# key: specify variable to be primary grouping
# index: tsibble compatible time variable in df
renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)
Let’s look at our timeseries data in a few different ways:
# this is the same as the ggplot we created before, done a different way
renew_ts %>% autoplot(value)
# breaks data into each "description" variable, by "month", across years
renew_ts %>% gg_subseries(value)
# look at season plot, within each season plot each year separately to see how things change
#renew_ts %>% gg_season(value) # we expected this to break...
# going to make the same graph with ggplot
ggplot(data = renew_parsed, aes(x = month, y = value, group = year))+
geom_line(aes(color = year))+
facet_wrap(~description,
ncol = 1,
scales = "free",
strip.position = "right") # description names on right
hydro_ts <- renew_ts %>%
filter(description == "hydroelectric power consumption")
hydro_ts %>% autoplot(value)
hydro_ts %>% gg_subseries(value)
#hydro_ts %>% gg_season(value) still not workin
ggplot(hydro_ts, aes(x = month, y = value, group = year))+
geom_line(aes(color = year))
hydro_quarterly <- hydro_ts %>%
index_by(year_qu = ~(yearquarter(.))) %>% # "." means "based on different groups that already exist"
summarize(avg_consumption = mean(value))
head(hydro_quarterly)
## # A tsibble: 6 x 2 [1Q]
## year_qu avg_consumption
## <qtr> <dbl>
## 1 1973 Q1 261.
## 2 1973 Q2 255.
## 3 1973 Q3 212.
## 4 1973 Q4 225.
## 5 1974 Q1 292.
## 6 1974 Q2 290.
dcmp <- hydro_ts %>%
model(STL(value ~ season(window = 5)))
components(dcmp) %>% autoplot()
# check distribution of remainder values, aiming for close to normal distribution
hist(components(dcmp)$remainder)
Now let’s look at the ACF:
hydro_ts %>%
ACF(value) %>%
autoplot()
# seasonality indicated in autocorrleation, observations that are 12 months apart are more highly correlated than observations that are any other distance apart
hydro_model <- hydro_ts %>%
model(
ARIMA(value),
ETS(value) # can add a second model to show different forecast outcomes
) %>%
fabletools::forecast(h = "4 years") #how long into the future do you want the forecast
hydro_model %>% autoplot(filter(hydro_ts, year(month_sep)>2010))
world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
layer = "TM_WORLD_BORDERS_SIMPL-0.3")
# mapview is a good quick way to view spatial data
mapview(world)